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149 changes: 149 additions & 0 deletions BIC_codes/FCS_estimate.py
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from functions.dFC_funcs import *
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@jeromedockes jeromedockes May 5, 2022

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in general avoid import * as it pollutes the namespace and makes it hard to know where names come from.
linting tools will complain about it; it's a good idea to use them -- have a look for example at pylint and flake8

import numpy as np
import time
import hdf5storage
import scipy.io as sio
import os
os.environ["MKL_NUM_THREADS"] = '64'
os.environ["NUMEXPR_NUM_THREADS"] = '64'
os.environ["OMP_NUM_THREADS"] = '64'

print('################################# CODE started running ... #################################')

################################# Parameters #################################

###### DATA PARAMETERS ######

output_root = './../../../../../RESULTs/methods_implementation/'
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@jeromedockes jeromedockes May 5, 2022

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  • consider putting paths in env variables or a config files rather than source files
  • use pathlib to manipulate filesystem paths

# output_root = '/data/origami/dFC/RESULTs/methods_implementation/'
# output_root = '/Users/mte/Documents/McGill/Project/dFC/RESULTs/methods_implementation/'

# DATA_type is either 'sample' or 'Gordon' or 'simulated' or 'ICA'
params_data_load = { \
'DATA_type': 'Gordon', \
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@jeromedockes jeromedockes May 5, 2022

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you don't need these backticks

'SESSIONs':['Rest1_LR' , 'Rest1_RL', 'Rest2_LR', 'Rest2_RL'], \

'data_root_simul': './../../../../DATA/TVB data/', \
'data_root_sample': './sampleDATA/', \
'data_root_gordon': './../../../../DATA/HCP/HCP_Gordon/', \
'data_root_ica': './../../../../DATA/HCP/HCP_PTN1200/node_timeseries/3T_HCP1200_MSMAll_d50_ts2/'
}

###### MEASUREMENT PARAMETERS ######

# W is in sec

params_methods = { \
# Sliding Parameters
'W': 44, 'n_overlap': 0.5, 'sw_method':'pear_corr', 'tapered_window':True, \
# TIME_FREQ
'TF_method':'WTC', \
# CLUSTERING AND DHMM
'clstr_base_measure':'SlidingWindow', \
# HMM
'hmm_iter': 50, 'n_hid_states': 24, \
# State Parameters
'n_states': 12, 'n_subj_clstrs': 20, \
# Parallelization Parameters
'n_jobs': 2, 'verbose': 0, 'backend': 'loky', \
# SESSION
'session': 'Rest1_LR', \
# Hyper Parameters
'normalization': True, \
'num_subj': 395, \
'num_select_nodes': 333, \
'num_time_point': 1200, \
'Fs_ratio': 1.00, \
'noise_ratio': 0.00, \
'num_realization': 1 \
}

###### HYPER PARAMETERS ALTERNATIVE ######

MEASURES_name_lst = [ \
'SlidingWindow', \
'Time-Freq', \
'CAP', \
'ContinuousHMM', \
'Windowless', \
'Clustering', \
'DiscreteHMM' \
]

alter_hparams = { \
# 'session': [], \
'n_states': [6], \
# 'normalization': [], \
# 'num_subj': [5], \
# 'num_select_nodes': [50], \
# 'num_time_point': [500], \
'Fs_ratio': [0.40], \
'noise_ratio': [2.00], \
# 'num_realization': [] \
}

###### dFC ANALYZER PARAMETERS ######

params_dFC_analyzer = { \
# VISUALIZATION
'vis_TR_idx': list(range(10, 20, 1)),'save_image': True, 'output_root': output_root, \
# Parallelization Parameters
'n_jobs': 8, 'verbose': 0, 'backend': 'loky' \
}


################################# LOAD DATA #################################

data_loader = DATA_LOADER(**params_data_load)
BOLD = data_loader.load()

################################# Visualize BOLD #################################

# for session in BOLD:
# BOLD[session].visualize(start_time=0, end_time=50, nodes_lst=list(range(10)), \
# save_image=params_dFC_analyzer['save_image'], output_root=output_root+'BOLD_signal_'+session)

################################# Measures of dFC #################################

dFC_analyzer = DFC_ANALYZER( \
analysis_name='reproducibility assessment', \
**params_dFC_analyzer \
)

MEASURES_lst = dFC_analyzer.measures_initializer( \
MEASURES_name_lst, \
params_methods, \
alter_hparams \
)

tic = time.time()
print('Measurement Started ...')

################################# estimate FCS #################################

task_id = int(os.getenv("SGE_TASK_ID"))
MEASURE_id = task_id-1 # SGE_TASK_ID starts from 1 not 0


if MEASURE_id >= len(MEASURES_lst):
print("MEASURE_id out of MEASURES_lst ")
else:
measure = MEASURES_lst[MEASURE_id]

print("FCS estimation started...")

time_series = BOLD[measure.params['session']]
if measure.is_state_based:
measure.estimate_FCS(time_series=time_series)

# dFC_analyzer.estimate_group_FCS(time_series_dict=BOLD)
print("FCS estimation done.")

print('Measurement required %0.3f seconds.' % (time.time() - tic, ))

# Save
np.save('./fitted_MEASURES/MEASURE_'+str(MEASURE_id)+'.npy', measure)
np.save('./dFC_analyzer.npy', dFC_analyzer)
np.save('./data_loader.npy', data_loader)

#################################################################################
172 changes: 172 additions & 0 deletions BIC_codes/dFC_assessment.py
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from functions.dFC_funcs import *
import numpy as np
import time
import hdf5storage
import scipy.io as sio
import os
os.environ["MKL_NUM_THREADS"] = '64'
os.environ["NUMEXPR_NUM_THREADS"] = '64'
os.environ["OMP_NUM_THREADS"] = '64'

print('################################# subject-level dFC assessment CODE started running ... #################################')

################################# Parameters #################################

# subj_id = '100206'

###### DATA PARAMETERS ######

output_root = './../../../../../RESULTs/methods_implementation/'
# output_root = '/data/origami/dFC/RESULTs/methods_implementation/'
# output_root = '/Users/mte/Documents/McGill/Project/dFC/RESULTs/methods_implementation/'

################################# LOAD #################################

dFC_analyzer = np.load('./dFC_analyzer.npy',allow_pickle='TRUE').item()
data_loader = np.load('./data_loader.npy',allow_pickle='TRUE').item()

################################# LOAD FIT MEASURES #################################

if dFC_analyzer.MEASURES_fit_lst==[]:
ALL_RECORDS = os.listdir('./fitted_MEASURES/')
ALL_RECORDS = [i for i in ALL_RECORDS if 'MEASURE' in i]
ALL_RECORDS.sort()
MEASURES_fit_lst = list()
for s in ALL_RECORDS:
fit_measure = np.load('./fitted_MEASURES/'+s,allow_pickle='TRUE').item()
MEASURES_fit_lst.append(fit_measure)
dFC_analyzer.set_MEASURES_fit_lst(MEASURES_fit_lst)
print('fitted MEASURES loaded ...')
# np.save('./dFC_analyzer.npy', dFC_analyzer)

################################# LOAD DATA #################################

task_id = int(os.getenv("SGE_TASK_ID"))
subj_id = data_loader.SUBJECTS[task_id-1] # SGE_TASK_ID starts from 1 not 0

BOLD = data_loader.load(subj_id2load=subj_id)

################################# dFC ASSESSMENT #################################

tic = time.time()
print('Measurement Started ...')

print("dFCM estimation started...")
dFCM_dict = dFC_analyzer.subj_lvl_dFC_assess(time_series_dict=BOLD)
# SUBJ_output = dFC_analyzer.group_dFCM_assess(time_series_dict=BOLD)
print("dFCM estimation done.")

print('Measurement required %0.3f seconds.' % (time.time() - tic, ))


################################# POST ANALYSIS #################################

SUBJ_output = {}

dFCM_lst = dFCM_dict['dFCM_lst']

# Save dFC samples
common_TRs = TR_intersection(dFCM_lst)
dFCM_sample_dict = {}
dFCM_sample_dict['common_TRs'] = common_TRs
dFCM_sample_dict['dFCM'] = {}
for i, dFCM in enumerate(dFCM_lst):
dFCM_sample = dFCM.get_dFC_mat(TRs=common_TRs)
dFCM_sample_dict['dFCM'][str(i)] = {}
dFCM_sample_dict['dFCM'][str(i)]['mat'] = dFCM_sample
dFCM_sample_dict['dFCM'][str(i)]['info'] = dFCM.measure.info
np.save('./dFC_samples/SUBJ_'+str(subj_id)+'_dFC.npy', dFCM_sample_dict)

########################## DEFAULT VALUES #######################

param_dict = dFC_analyzer.params_methods
analysis_name_lst = [ \
'corr_mat', \
'dFC_distance', \
'dFC_distance_var', \
'FO', \
'CO', \
'TP', \
'trans_freq' \
]
dFCM_lst2check = filter_dFCM_lst(dFCM_lst, **param_dict)
SUBJ_output['default_values'] = dFC_analyzer.post_analysis( \
dFCM_lst=dFCM_lst2check, \
analysis_name_lst=analysis_name_lst \
)

########################## 6_states #######################

param_dict = {'n_states': [6], 'is_state_based': [True]}
analysis_name_lst = [ \
'corr_mat', \
'dFC_distance', \
'dFC_distance_var', \
'FO', \
'CO', \
'TP', \
'trans_freq' \
]
dFCM_lst2check = filter_dFCM_lst(dFCM_lst, **param_dict)
SUBJ_output['6_states'] = dFC_analyzer.post_analysis( \
dFCM_lst=dFCM_lst2check, \
analysis_name_lst=analysis_name_lst \
)

########################## SlidingWindow_100_nodes #######################

param_dict = {'measure_name': ['SlidingWindow'], 'num_select_nodes': [100]}
analysis_name_lst = [ \
'corr_mat', \
'dFC_distance', \
'dFC_distance_var', \
'FO', \
'CO', \
'TP', \
'trans_freq' \
]
dFCM_lst2check = filter_dFCM_lst(dFCM_lst, **param_dict)
SUBJ_output['SlidingWindow_100_nodes'] = dFC_analyzer.post_analysis( \
dFCM_lst=dFCM_lst2check, \
analysis_name_lst=analysis_name_lst \
)

########################## Fs_ratio_0.5 #######################

param_dict = {'Fs_ratio': [0.5]}
analysis_name_lst = [ \
'corr_mat', \
'dFC_distance', \
'dFC_distance_var', \
'FO', \
'CO', \
'TP', \
'trans_freq' \
]
dFCM_lst2check = filter_dFCM_lst(dFCM_lst, **param_dict)
SUBJ_output['Fs_ratio_0.5'] = dFC_analyzer.post_analysis( \
dFCM_lst=dFCM_lst2check, \
analysis_name_lst=analysis_name_lst \
)

########################## noise_ratio_1 #######################

param_dict = {'noise_ratio': [1.0]}
analysis_name_lst = [ \
'corr_mat', \
'dFC_distance', \
'dFC_distance_var', \
'FO', \
'CO', \
'TP', \
'trans_freq' \
]
dFCM_lst2check = filter_dFCM_lst(dFCM_lst, **param_dict)
SUBJ_output['noise_ratio_1'] = dFC_analyzer.post_analysis( \
dFCM_lst=dFCM_lst2check, \
analysis_name_lst=analysis_name_lst \
)

# Save
np.save('./dFC_assessed/SUBJ_'+str(subj_id)+'_output.npy', SUBJ_output)
#################################################################################
Empty file added BIC_codes/functions/__init__.py
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